The role of time-varying nanofluid flow and particle aggregation on the kinetics of homogeneous and heterogeneous reactions in position-controlled squeezing porous slider

The functionality of clutch and transmission mechanisms heavily relies on permeable sliders, which are designed to optimize load support and reduce energy losses due to friction. This research delves into nanofluid dynamics across an expansion/contraction permeable slider with the impact of magnetic field, thermal radiation, and non-uniform heat absorption/generation. Examining nanoparticle aggregation and homogeneous-heterogeneous chemical reactions provides a more comprehensive understanding of fluid flow mechanisms. A significant contribution of this research is its implementation of a slider-position-dependent fluid injection technique for superior levitation regulation. The governing partial differential equations (PDEs) are transformed into dimensionless ordinary differential equations (ODEs) through similarity transformations, and the resultant ODEs are solved using Runge Kutta Fehlberg’s fourth-fifth order (RKF-45) numerical method. Furthermore, the study employs an artificial neural network (ANN) model with the Levenberg-Marquardt backpropagation learning strategy to analyze the heat transfer rate. Graphical representations show that enhanced wall dilation causes velocity profiles to decline. Rising the radiation parameter increases the thermal profile. The effectiveness of the numerical model is demonstrated through Nusselt number evaluations, with recorded mean square error values. These results provide critical guidance for optimizing thermal performance in precision-engineered mechanical components.